A branch-and-bound approach for maximum quasi-cliques

Foad Mahdavi Pajouh (), Zhuqi Miao () and Balabhaskar Balasundaram ()

Annals of Operations Research, 2014, vol. 216, issue 1, 145-161

Abstract: Detecting quasi-cliques in graphs is a useful tool for detecting dense clusters in graph-based data mining. Particularly in large-scale data sets that are error-prone, cliques are overly restrictive and impractical. Quasi-clique detection has been accomplished using heuristic approaches in various applications of graph-based data mining in protein interaction networks, gene co-expression networks, and telecommunication networks. Quasi-cliques are not hereditary, in the sense that every subset of a quasi-clique need not be a quasi-clique. This lack of heredity introduces interesting challenges in the development of exact algorithms to detect maximum cardinality quasi-cliques. The only exact approaches for this problem are limited to two mixed integer programming formulations that were recently proposed in the literature. The main contribution of this article is a new combinatorial branch-and-bound algorithm for the maximum quasi-clique problem. Copyright Springer Science+Business Media New York 2014

Keywords: Clique; Quasi-clique; Cluster detection; Graph-based data mining (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10479-012-1242-y

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